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Recent approaches in depth-based human activity analysis achieved outstandingperformance and proved the effectiveness of 3D representation forclassification of action classes. Currently available depth-based andRGB+D-based action recognition benchmarks have a number of limitations,including the lack of training samples, distinct class labels, camera views andvariety of subjects. In this paper we introduce a large-scale dataset for RGB+Dhuman action recognition with more than 56 thousand video samples and 4 millionframes, collected from 40 distinct subjects. Our dataset contains 60 differentaction classes including daily, mutual, and health-related actions. Inaddition, we propose a new recurrent neural network structure to model thelong-term temporal correlation of the features for each body part, and utilizethem for better action classification. Experimental results show the advantagesof applying deep learning methods over state-of-the-art hand-crafted featureson the suggested cross-subject and cross-view evaluation criteria for ourdataset. The introduction of this large scale dataset will enable the communityto apply, develop and adapt various data-hungry learning techniques for thetask of depth-based and RGB+D-based human activity analysis.

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis